1. Field of Invention
The present invention relates to systems and methods for medical information processing and analysis. Specifically, the present invention relates to intelligent qualitative and quantitative analysis of medical information for medical decision making.
2. Description of Related Art
In disease diagnosis using modem imaging techniques, physicians are often overwhelmed by the amount of information made available through different acquisition devices. Such devices may include, but are not limited to, ultrasound (US), Computerized Tomography (CT), and Magnet Resonance Imaging (MRI). Diagnostic information (DI), may differ from patient data and it may include key diagnostic evidence identified from diagnostic data that directly or indirectly supports and/or disaffirms a physician/specialist's hypothesis about a diagnosis. Often, important diagnostic information relevant to a specific disease is buried in the huge volume of data. In addition, although patient records and laboratory test results, such as blood tests, may provide important clues to suspected diseases/abnormalities, the interpretation of such information is not conventionally integrated with various image-based diagnosis processes in a coherent fashion. Consequently, physicians have to look manually for all disease-relevant information embedded in both non-visual and visual data from different sources. This task is labor intensive and requires a high level of skill. In addition, a manual process is also subject to mistakes, which may lead to misdiagnosis due to either negligence or lack of skill, qualitative and quantitative measurements, as well as intuitive visualization means that allow a physician to seamlessly integrate information across multi-modalities into a clinic workflow.
Another problem of the prior art is that conventional Computer Aided Detection/Diagnosis (CAD) systems usually allow a physician to access only either a computer's output in the form of binary decisions or raw data. That is, a wide variety of rich DI that can be derived between the raw data and the binary decision output (i.e., yes or no decisions) is ignored or discarded. Since such a loss is irreversible, the physician does not have the opportunity to interactively and quantitatively examine or identify suspicious regions, with the assistance of a computer system, in order to make their independent decision.
A third problem with conventional CAD systems is that multi-modality information, including both non-visual and visual information, is not utilized simultaneously. Current CAD systems usually operate on data of a single modality.
FIG. 1 illustrates a flowchart of a conventional computer-aided detection system, in which a physician starts, at 104, by selecting a patient image of a particular modality with respect to a pre-defined disease. At 106, a computer detects suspicious regions in the image that may correspond to where a specific disease manifests itself. The result of such computer detection is a binary yes/no decision for each location in the image. When the computer indicates the existence of an abnormality in an image location, a mark may be displayed, at 108, near the corresponding location in the image. At 110, the physician makes diagnostic decisions based on the computer derived marks.